Abstract—Addressing the needs of students in entry-level

Computer Science courses, an online social-network-based

learning environment, PeerSpace, was successfully developed to

enhance student learning and performance. PeerSpace

integrates a suite of Web 2.0 tools that promote student

interactions on course-related topics as well as purely social

matters. Part of this suite is a practice tool, Preparation Station,

developed to strengthen and reinforce students’ grasp of

concepts learned in class, to encourage student participation in

PeerSpace, and to trigger course-related communication among

students. PeerSpace is implemented atop the open-source Elgg

social network framework. This paper presents design and

implementation details of Preparation Station as well as an

assessment of its usefulness and effectiveness.

I. INTRODUCTION

Education research provides evidence that interacting with

peers fortifies the learning process and makes learning more

enjoyable [1]-[2]. However a survey of the culture among

students in introductory Computer Science (CS) courses has

revealed negative behaviors such as: disdain for working in

groups, unwillingness to support or aid others and

combativeness towards the opinions of peers [3]. This type of

peer learning environment has an adverse effect on student’s

motivation, persistence, and passion towards the course

material. Faced with high dropout and failure rates [4],

computer science educators have sought for a variety of

methods to improve the situation by developing innovative

teaching pedagogies and effective teaching and learning tools

and environments.

PeerSpace is an online learning environment developed to

enhance student learning by encouraging and facilitating the

building of peer support networks among students enrolled in

entry level CS courses. Peer networks enable the students to

support each other both socially and academically and to deal

with common difficulties such as stress and isolation. In

addition, peer networks serve as a solid foundation for

Manuscript received October 15, 2012; revised December 14, 2012. This

work is supported by the CCLI-TUES program of National Science

Foundation under grant DUE-0837385 and a grant from the Sponsored

Research Program at Middle Tennessee State University.

Cen Li, Zhijiang Dong, and Roland H. Untch are with the Department of

Computer Science, Middle Tennessee State University, Murfreesboro, TN

37132 USA (e-mail: Cen.Li@ mtsu.edu, Zhijiang.Dong@mtsu.edu,

Roland.Untch@ mtsu.edu).

Divya Jagadeesh was a graduate student in Middle Tennessee State

University. She is now with Hospital Corporation of America, Nashville,

Tennessee 37203 USA (e-mail: divyajagadeesh@gmail.com).

effective peer collaborative learning. With strong peer

support, the students will be more comfortable and willing to

share knowledge and experiences, exchange ideas, and seek

help [5]. Carefully designed learning activities in PeerSpace

encourage students to help each other and learn from each

other. A combination of these is expected to lead to enhanced

student learning.

In order to give students opportunities to reinforce course

concepts, encourage student participation in PeerSpace, and

to trigger course-related communication among students, an

online practice tool, Preparation Station, was created. This

tool helps the students gain additional knowledge on

concepts covered in class by working on a set of

course-related practice questions. The questions,

administered via this component of PeerSpace, are selected

by the instructor according to the current needs of the class.

Most existing online practice tools have been developed as

stand-alone applications [6]-[7]. Others have been integrated

into larger learning systems. Some of these systems were

developed and distributed commercially while others were

developed in-house in order to support specific

technology-mediated collaborative learning programs. Key

examples in the first category are Blackboard™ [8], ATutor

[9] and Sakai [10]. The WebTycho system developed by the

University of Maryland [11] is an example of in-house

systems. Rossling et al. [12] conducted an online survey to

determine “the nature of computer science educators'

attitudes toward learning management systems”.

Respondents to the survey claimed that “large scale LMS’s

were not flexible enough and that they did not cover the

entire spectrum of what was required for computer science

education” [12]. Respondents also stated that “cooperation

and data exchange between LMS and other computer science

specific tools was not very good” [12]. From the survey

responses the authors concluded that there is a lot of scope for

“integrating computer science specific tools with the broader

and more general capabilities of LMSs” [12].

The Preparation Station tool in the PeerSpace

environment is designed based on the fundamental ideas of a

learning content management system and online social

networks. The various sub-components of the Preparation

Station tool in combination with the social networking

features of the PeerSpace environment provide various

LCMS features such as web-based course content delivery,

practice test handling, assessment and report generation,

score display and student discussion forums. Also, since

PeerSpace is developed based on Elgg, an open source social

networking framework, it is extensible and allows the

addition of CS specific functionality through Plugins. The

PeerSpace environment provides for seamless integration

Developing an Interactive Practice Tool in PeerSpace for

First Year Computer Science Students

Cen Li, Zhijiang Dong, Roland H. Untch, and Divya Jagadeesh

International Journal of Information and Education Technology, Vol. 3, No. 1, February 2013

48DOI: 10.7763/IJIET.2013.V3.232

Index Terms—Computer supported collaborative learning,

computer uses in education, online learning tool, CS1 and CS2.

International Journal of Information and Education Technology, Vol. 3, No. 1, February 2013

49

between the various student collaborative activities and

course management features.

We have implemented the Preparation Station tool within

the PeerSpace learning environment. Students may seek help

on these exercises from the other students in the learning

community through online communication channels such as

group discussion forum and online chats. Students may serve

as tutors to other students by answering email, or by posting a

reply to a question on the course discussion forum. Students

will benefit from the convenience and flexibility offered by

anytime, anywhere access.

II. PEERSPACE

PeerSpace is developed based on Elgg [13], an open

source social networking framework. Elgg comes with a

number of basic features, some of which are directly

applicable in Peerspace. These include:

User profile: Students may edit their profile information

such as the profile icon, interests, email, and personal

web links;

Friends: Students can request/add other students to be

their friends;

Personal blogs: Students may post blogs with

multimedia content and comment on other students’

blogs;

Groups: Users may create groups or join existing groups.

The group forum discussion feature allows a private

discussion among the group members.

To make PeerSpace an environment that foster a strong

sense of community among the freshmen students taking the

CS courses, a number of new features have been developed

as additional plugin modules and widgets:

Scoring for PeerSpace community contribution to keep

track of a student’s participation/contribution to the

community;

Groups with high average member contribution scores

are shown on the front page of PeerSpace;

A custom-built forum discussion module that allows

hierarchical threading and thumbs up and down voting;

An online chat in PeerSpace is achieved by running an

OpenFire server and the Pidgin clients. The online chat

feature is directly connected to Peerspace where the

users and groups created within PeerSpace are

automatically created as users and groups within the

OpenFire database.

The group wiki plugin has been adapted to allow the

study group members to collaboratively creating/editing

wiki documents.

Once logged in, a student is presented with the PeerSpace

front page. The PeerSpace front page is designed to show the

current community status, e.g., latest blog and forum posts,

online users, list of friends, group membership; as well as the

current rankings of the top students and top groups. Fig. 1

shows a snapshot of the PeerSpace front page. Each

component in the front page (enclosed in a shaded rectangle)

is built as an Elgg widget. Students may customize the

features of individual widgets as well as the layout of the

front page with an easy drag-and-drop interface.

III. PREPARATION STATION

The Preparation Station tool is a new component of

PeerSpace designed to help the students better understand the

course material by working on additional course related

exercises outside of class. The exercises are designed by the

instructor according to the current needs of the class. Each

exercise is a collection of questions on the same topic, i.e.,

array. In addition to helping the students reinforce the

material taught in class, the exercises are designed to trigger

course related discussion among students on PeerSpace

leading to more peer communication, collaboration and peer

tutoring. The Preparation Station tool has been designed such

that it is convenient for an instructor to:

create, edit, organize, and view the questions;

create exercises using subsets of the questions, and order

the questions within each exercise; and

create and edit assignments for the intended student

groups and with appropriate due dates.

A student can start and stop working on the assignment at

any time and resume later. When student resumes the

assignment, the questions start from the point where the

assignment was left off. Students will receive immediate

feedback on the correctness of the answer as well as an

explanation to the question if the answer is incorrect.

Information about assignment progress, for example total

number of questions answered, total correct answers and

number of questions remaining, is available for the students

at any time. If the instructor wishes to allow the students to

work on the questions more than once, a reset option is

available for the students to work through the questions

again.

A. Create / Edit / Delete Questions

Separate templates for creation of multiple choice

questions and true/false questions are designed. The multiple

choice question template has provision for entering the

question, its choices, the correct answer, a small explanation

of the concept behind the questions for students, comments

for instructor reference, the difficulty level of the question

and a provision to choose a keyword from an existing list of

keywords to indicate the concept covered by the question.

The true/false question template has a similar format. Instead

of the fields for the possible choices of a question, a field is

provided for the correct answer. Preview of the question is

made available in the template pages to allow the user to

Fig. 1. PeerSpace front page

International Journal of Information and Education Technology, Vol. 3, No. 1, February 2013

50

preview the questions while creating them. The questions

used in Preparation Station are taken mostly from the test

bank questions supplied with the textbook by Dale [14]. Fig.

2 shows the template designed for the creation of a

multiple-choice question.

B. Create / Edit / Delete Exercises

An exercise contains a set of questions covering one or

more concepts. Each exercise has a title and is linked to one

or more existing keywords. Linking an exercise to a keyword

includes all the questions associated with that keyword as

part of that exercise. In addition, the instructor may choose to

include or exclude certain questions for the exercise

depending on the progress of a class.

Although all the questions associated with the keywords

chosen for an exercise get included in the exercise, the

questions are all disabled by default at first. An instructor can

enable or disable all the questions, or only a selected number

of questions for the exercise. The instructor is also able to

order the questions selected for the exercise by drag and drop

the question to the desired position. Deletion of an exercise

also deletes the details of all the assignments that are linked to

the exercise. Exercise deletion is not allowed if an exercise is

linked to a current assignment, and the students have been

working on the assignment.

C. Create / Edit / Delete Assignments

An assignment assigns an exercise to one or more groups

of students. Each assignment is given a submission deadline.

The students in the selected groups will be able to work on

the assignment till the specified deadline. A mechanism is

built-in for the instructor to specify whether (s)he wishes to

allow or disallow the students to reset their work on the

assignment.

Fig. 3. An instructor’s view of all the exercises created

Fig. 3 shows a list of assignments created for various

classes during Fall 2010. Click on the “edit” button of an

assignment leads to a page for editing the title, student groups,

exercise, as well as the deadline of the assignment. Click on

the “delete” button deletes all information relates to that

assignment. To prevent accidental deletion of students’ work

on their assignments, information about the students’

progress on an assignment is displayed before a confirmation

is requested for deletion.

D. Student Performance Analysis

The Assignment Analysis module helps the instructors

assess the student performance on the assignments. Student

progress information such as total number of questions

answered and total correct number of questions answered are

recorded for all the assignments. The Analysis-By-Student

option allows an instructor to view one student’s

performance on a select exercise. The Analysis-by-Group

option allows an instructor to view a cumulative report of the

performance of all the students in one group on all the

exercises the group worked on. The report lists all the

assignments that the group has worked on during the

specified time period. For each assignment, the report shows

the ids of the questions in the assignment, the total number of

students who gave the correct answer, as well as the

distribution of the student responses across different answer

choices. The Analysis-by-Exercise option allows an

instructor to view the performance of all the student groups

that have worked on one chosen exercise in a given time

period. In the report, for each group, the ids of the questions

in the assignment, the total number of students who gave the

correct answer to the question, as well as the distribution of

the student responses across different answer choices are

displayed. Fig. 4 shows an example of Analysis-by-Group

for one CS1 class group.

Fig. 4. An example report summarizing the performance of a group on an

assignment

IV. IMPLEMENTATION

A. The Elgg Framework

PeerSpace is developed based on Elgg [13], an open

source social networking framework which provides the

basic functionality to run a social network. Elgg is extensible

which allows it to be customized with different features

through the use of plugin modules.

Elgg is primarily implemented using PHP scripting

language and supports handling of the backend database

using MySQL. The main challenge encountered with

development using Elgg is, by design, creation of one’s own

database tables is not recommended/allowed. This is because,

“Elgg is underpinned by a flexible, generic data model” due

to which plugins and custom code need not be updated “if

Fig. 2. A template for creating multiple-choice questions

International Journal of Information and Education Technology, Vol. 3, No. 1, February 2013

51

new database functionality is introduced, or if a new data

back-end is inserted to support multiple servers or other

infrastructure requirements” [13].

B. The Elgg Data Model

Elgg is running on a unified data model, based on atomic

units of data called entities. The top level class is called

ElggEntity, which defines for all entities the most common

properties and behaviors such as GUID (Globally Unique

Identifier) to identify a ElggEntity object uniquely, access

permissions to control object access, an arbitrary subtype to

group similar entities, an owner, and the site that the entity

belongs to. ElggEntity has four main specializations, which

provide extra properties and methods to handle different

kinds of data: ElggObject to manage objects like blog posts,

uploaded files, questions, assignments, and exercises;

ElggUser to manage users in the system; ElggSite for each

site within an Elgg install; and ElggGroup to manage user

groups.

Entities can be extended with extra information using

Metadata and Annotations. Metadata “is information that is

added to an object to describe it further” [13]. Examples of

metadata include question type, assignment deadline, and

number of questions answered. Annotations “is the

information generally added by third parties to the

information provided by the entity” [13]. For example,

comments and ratings of a blog post are both annotations.

In order to make the system developed using Elgg more

stable, plugin modules are strongly discouraged from dealing

with database directly. Any access to the database should be

done using the predefined functions available to handle

objects in Elgg. Such an approach allows for content created

by different plugins to be mixed together in consistent ways

[4].

C. Implementation of the Student Interface

Each assignment in the PeerSpace environment is stored

as an ElggObject of subtype “assignment”. In addition to the

predefined properties of an ElggObject such as GUID, access

permissions, owner, and title, the following set of metadata

have been created for each assignment entity:

Groupid – an array of guids of all the groups to which

the assignment is assigned to;

Exeid – the guid of the exercise that is used in the

assignment;

Deadline – the deadline date for the assignment;

Resetoption – set to 0 if students are not allowed to work

multiple times on the assignment, and to 1 otherwise;

and

Questionguid – an array of guids of all the enabled

questions in the assignment.

A student is allowed to view only the assignments that are

assigned to him, i.e., assignments assigned to the groups in

which the student is a member of. The assignment display

screen (similar to that of Fig. 3) displays the title and deadline

for each of the retrieved assignment using the assignment

object’s “title” property and “deadline” metadata. A “Reset”

button is also displayed alongside the title if the “resetoption”

metadata of the assignment is set to 1. Otherwise, a “not

allowed” message is displayed in that column.

When a student first works on an assignment, an

ElggObject is created to hold the student’s assignment

progress and performance details. This ElggObject has

subtype “testdetails”. Metadata are created for this object to

record the GUID of the user; the GUID of the assignment; the

GUID of the exercise the assignment is linked to; the total

number of questions answered; the percentage of the

questions answered correctly; the ids of the questions in the

order answered by the student; as well as the student answers.

When a student starts working on an assignment, the

assignment GUID is used to retrieve the corresponding

assignment object. Questions in the assignment are retrieved

using the “questionguid” metadata of the “assignment”

object and displayed one at a time to the student. Fig. 5.

shows how the questions are presented to the students one at

a time.

Questions are stored as ElggObjects of subtype “Question”

with metadata created for information including the answer

for the question; the question choices; the question type, as

well as the answer explanation. The question choices and the

assignment progress information are populated using the

metadata of the “question” and “testdetails” objects

respectively. When a student submits an answer, the

student’s answer is verified against the “answer” metadata of

the “question” object and the metadata values for the

student’s “testdetails” object are updated accordingly. The

feedbacks for the student’s answer are also updated from the

corresponding metadata of the “question” object.

A student can exit the assignment at any time. When a

student resumes an assignment, the “testdetails” object

created for the student is retrieved based on the assignment’s

GUID and the student user’s GUID. When a student reset his

assignment, the “testdetails” object of the student for the

assignment is retrieved. Then the “testdetails” object is

deleted.

V. EXPERIMENTAL ANALYSIS

PeerSpace has been used by CS1 and CS2 students in the

Spring and Fall 2010 semesters. In each semester, two CS1

sections were designated the Experiment and the Control

group at the beginning of the semester. The two sections in a

semester were taught by the same instructor with the same

lecture material and textbook, albeit the instructors

participated in the study in the Spring and the Fall semester

are two different person. In both semesters, the Preparation

Fig. 5. The exercises are presented to students one question at a time in

preparation station

International Journal of Information and Education Technology, Vol. 3, No. 1, February 2013

52

Station tool was used mainly as a test preparation tool with

assignments given to the students prior to tests.

When analyzing the aggregated results from two groups of

students from these two semesters, the two-tailed t-test was

used to compute the statistical significance between the mean

values obtained from the two groups. If the test scores of the

group of students who used the practice tool differs from that

of the group of students who did not use to the tool, the test

helps to answer the question weather any difference observed

is statistically significant.

Table I summarizes the comparison results between the

control group and the experiment group from the two

semesters. In the Spring 2010 semester, the mean score for

the students in the experiment group for both Test 3 and Final

Exam, 33.1 (out of 50 points) and 75.1, are higher than those

from the students in the control group, 29.3 and 72

respectively. Similarly, in Fall 2010, the mean score for the

students in the experiment group for Test 2 and Test 3, 75.4

and 62.4, are higher than those from the students in the

control group, 70.7 and 59.8. The last column of the table

presents the t-test results of these comparisons. The t-test

results suggest that the higher mean scores observed from all

four tests for the experiment group are not statistically

significant with r-values of 0.296, 0.656, 0.45 and 0.76

respectively.

TABLE I: COMPARISONS OF THE CONTROL AND EXPERIMENT GROUP TEST

SCORES FROM 2010 SPRING AND FALL SEMESTER

Tests Group Sample

Size

Mean

(std dev)

r-value

Spring

2010

Test 3

Control 18 29.3(11.1)

0.29 Experiment 21 33.1(11.0)

Final

Exam

Control 16 72(18.5)

0.65 Experiment 17 75.1(19.7)

Fall

2010

Test 2

Control 21 70.7(24.8)

0.45 Experiment 26 75.4(17.8)

Test 3

Control 18 59.8(29.4)

0.76 Experiment 24 62.4(26)

Student surveys have been conducted at the end of each

semester to gather student opinions about the usefulness of

Preparation Station in learning the course material and

preparing for tests. Fig. 6 summaries the results. For the

statement “the Preparation Station tool helped me in

understanding the course material better”, a combined 63%

of the students gave positive opinion, with 30% of these

students strongly agreeing with the statement (Fig. 6 (a) ).

For the statement “after using the Preparation Station tool, I

feel more confident about taking a test on this material”, a

similar trend is observed with 28% of the students strongly

agreeing and another 31% of the students agreeing (Fig. 6

(b) ). When asked their opinion about the social aspect of the

tool, “the preparation station tool benefits me more since I

can discuss the solutions and clarify concepts with my

classmates when working through the questions”, 23% of the

students strongly agree the statement and another 30% of the

students agreeing. These results show that a majority of the

students found Preparation Station tool to be a useful tool for

learning course material and preparing for the tests. It is also

observed that for each survey statement, the answers from

approximately 30% of the students show neutral or negative

responses. When examining the student performance data in

Preparation Station, it was found that many of the students

who did not answer positively in the survey did not work on

or complete the Preparation Station assignments, mainly due

to the fact that the Preparation Station assignments are given

as voluntary assignments. For example in Spring 2010, 30%

of the students fully utilized the Preparation Station tool and

65% used it partially. The Preparation Station usage is

higher in the Fall 2010 semester. It is understandable that the

students cannot realize the benefits of the tool unless they use

it.

Fig. 7. Comparing the experiment and control groups on the amount of

learning occurred during studying

A secondary objective of implementing the Preparation

Station tool within PeerSpace is to promote peer learning.

Students having questions while working through the

Preparation Station questions can ask for help in PeerSpace

Fig. 6. Student survey results concerning the usage and usefulness of preparation

station for learning in CS1

International Journal of Information and Education Technology, Vol. 3, No. 1, February 2013

53

through public (PeerSpace site-wide) or class group

discussion forum, chat online with fellow students, or talk to

them in person. Figure 7 presents the comparison results

between the experiment and the control group students in

terms of the amount of peer learning occurred during test

preparation. It can be seen that the percentage of students

who has consulted with their peers for 3-5 times or for more

than 5 times during studying is higher among the experiment

group students than among the control group students. There

is also a slightly higher percentage of students never

discussed course related concepts with their classmates

during test preparation. One explanation may be because if

one answers a Preparation Station question incorrectly, a

detailed explanation about that question is always presented,

which maybe sufficient for many students to clear their

misconception, therefore no need to consult a fellow student.

Additional investigation is needed to fully understand this

data.

VI. CONCLUSIONS

In this paper, the motivation, design, and implementation

of an online practice tool developed atop the Elgg social

network engine is presented. In addition, comparative

experiments have been conducted with CS1 students in two

semesters. Overall from the student survey responses, it can

be seen that the students did perceive Preparation Station as

a good and useful learning tool, especially those who made

full use of the tool. For the Experiment and the Control

groups of students, the mean test scores of the students in the

Experiment group was consistently higher than the mean test

scores of the students in the Control group (although

variations in individual performance rendered this difference

as not statistically significant.) Although performance

improvement have been observed in the students who

worked on Preparation Station exercises for test preparation,

the improvement in student performance in a whole was not

up to the level we had initially anticipated. The benefits of the

Preparation Station tool on student learning are highly

dependent on the level of participation. In the future

experimentation, it is essential that the Preparation Station

assignment be made mandatory, at least initially, so that the

students have a chance to use it and realize for themselves

that it is helpful.

REFERENCES

[1] A. Perez-Prado and M. Hirunarayanan, “A qualitative comparison of

online and classroom-based sections of a course: Exploring student

perspectives,” Educational Media International, vol. 39, no. 2, pp.

195-202, 2002.

[2] K. Swan, “Building learning communities in online courses: the

importance of interaction,” Education Communication and

Information, vol. 2, no. 1, pp. 23-49, 2002.

[1] W. M. Waite and P. M. Leonardi, “Student culture vs. group work in

computer science,” in Proc. 35th SIGCSE Technical Symposium on

Computer Science Education, Norfolk, VA, USA, March 2004, pp.

12-16.

[2] P. Kinnunen and L. Malmi, “Why students drop out CS1 course?” in

Proc. of the 2nd International Workshop on Computing Education

Research, Canterbury, UK, Sept. 2006, pp. 97-108.

[3] V. Tinto, “Classroom as communities: exploring the educational

character of student persistence,” Journal of Higher Education, vol. 68,

no. 6, pp. 599-623, 1997.

[4] J. Yoo, C. Pettey, S. Yoo, J. Hankins, C. Li, and S. Seo, “Intelligent

Tutoring System for CS-I and II Laboratory,” in Proc. 44th ACM

Southeast Conference, Melbourne, FL, March 10-12th, 2006, pp.

186-191.

[5] J. Yoo, C. Li, and C. Pettey, “Adaptive teaching strategy for online

learning,” in Proc. International Conference on User Interface, San

Diego, CA, Jan. 2005, pp. 266-268.

[6] BlackboardTM. (November 20th, 2012). [Online]. Available:

http://www.blackboard.com.

[7] Atutor. (November 20th, 2012). [Online]. Available:

http://www.atutor.ca. Accessed

[8] Sakai. (July 27 2012). [Online]. Available:

http://www.sakaiproject.org. Accessed

[9] WebTycho. (July 27 2012). [Online]. Available:

http://www.makli.com/webtycho-0035823. Accessed

[10] G. Rossling, L. Malmi, and M. Clancy, “Enhancing learning

management systems to better support computer science education,”

ACM SIGCSE Bulletin, vol. 40, no. 4, pp. 142-166, 2008.

[11] N. Dale and C. Weems, Programming and problem solving with C++,

Sudbury, MA: Jones and Bartlett Publishers, 2010.

[12] Elgg: (November 20th, 2012). A Powerful Social Engine. [Online].

Available: http://www.elgg.org.

Cen Li

obtained a M.S. and a Ph.D. in computer

science from Vanderbilt University (Nashville, TN,

USA) in 1995 and 2000 respectively. She has a B.S.

degree in computer science from Middle Tennessee

State University (Murfreesboro, TN, USA) 1993. Dr.

Li is currently a Professor in the Department of

Computer Science at the Middle Tennessee State

University. Her main research interests are machine

learning, data mining, information retrieval and robotics. Dr. Li is a member

of the ACM.

Zhijiang Dong

obtained Ph.D. in Computer Science

from Florida International University (Miami, FL,

USA) in 2006, M.S. in Computer Science and B.S. in

Applied Mathematics from Huazhong University of

Science and Technology (China) in 1997 and 1994

respectively. Dr. Dong’s research interests include

software engineer, formal methods, verification and

validation. Dr. Dong is currently an Associate

Professor in the Department of Computer Science at

Middle Tennessee State University. Dr. Dong is a member of the ACM.

Roland H. Untch

has a Ph.D. in computer science

from Clemson University (Clemson, South Carolina)

1995, an M.S. in computer science from DePaul

University (Chicago, Illinois) 1983, and a B.A. in

management from Mundelein College/Loyola

University of Chicago (Chicago, Illinois) 1979. He is

currently a Professor in the computer science

department of Middle Tennessee State University. Dr.

Untch is a member of the ACM and the IEEE Computer Society.

Divya Jagadeesh

was born in January 1, 1985, in

Chennai, India. She graduated with a Bachelor of

Science in Computer Science degree from Anna

University, Chennai, India in 2006 and a Master of

Science in Computer Science degree from Middle

Tennessee State University, Murfreesboro, Tennessee,

USA in 2010. She worked as a Programmer Analyst in Cognizant

Technology Solutions from August, 2006 to December,

2007. She also worked as a Research Assistant under the guidance of Dr. Cen

Li in Middle Tennessee State University from June, 2009 to May, 2010. She

currently works as a Senior Report Solutions Analyst in Hospital

Corporation of America, Nashville, Tennessee. Her current and previous